Original Source Material As a further example, APT queries and their results may be used to form rules for expert systems that become part of an intelligent computer-based instructional system. Such a system theoretically can optimize student learning by recommending instructional sequences (i.e., temporal patterns) that have high probabilities of resulting in student mastery. In other words, APT- based decision making by a computer program can provide an empirical foundation for artificial intelligence. References: Frick, T. W. (1990). Analysis of patterns in time: A method of recording and quantifying temporal relations in education. American Educational Research Journal, 27(1), 180-204. Student Version One way that learning can be personalized is through the use of computers to aid in "recommending instructional sequences (i.e., temporal patterns) that have high probabilities of resulting in student mastery" (Frick, 1990, p. 202). However, the ability for computers to make appropriate decisions about instructional strategies is limited, in part, by the quality of information they have access to. References: Frick, T. W. (1990). Analysis of patterns in time: A method of recording and quantifying temporal relations in education. American Educational Research Journal, 27(1), 180-204